Statistical approach to inverse distance interpolation

Inverse distance interpolation is a robust and widely used estimation technique. Variants of kriging are often proposed as statistical techniques with superior mathematical properties such as minimum error variance; however, the robustness and simplicity of inverse distance interpolation motivate its continued use. This paper presents an approach to integrate statistical controls such as minimum error variance into inverse distance interpolation. The optimal exponent and number of data may be calculated globally or locally. Measures of uncertainty and local smoothness may be derived from inverse distance estimates.     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

A comparison of three-dimensional interpolation techniques for plume characterization

Interpolation of contaminant data can present a significant challenge due to sample clustering and sharp gradients in concentration. The research presented in this paper represents a study of commonly used interpolation schemes applied to three-dimensional plume characterization. Kriging, natural neighbor, and inverse distance weighted interpolation were tested on four actual data sets. The accuracy of each scheme was gauged using the cross-validation approach. Each scheme was compared to the other schemes and the effect of various interpolation parameters was studied. The kriging approach resulted in the lowest error at three of the four sites. The simpler and quicker inverse distance weighted approach resulted in a lower interpolation error on the other site and performed well overall. The natural neighbor method had the highest average error at all four sites in spite of the fact that it has been shown to perform well with clustered data. Another unexpected result was that the computationally expensive high order nodal functions resulted in reduced accuracy for the inverse distance weighted and natural neighbor approaches.     

Topographic accuracy assessment of bare earth lidar-derived unstructured meshes

This study is focused on the integration of bare earth lidar (Light Detection and Ranging) data into unstructured (triangular) finite element meshes and the implications on simulating storm surge inundation using a shallow water equations model. A methodology is developed to compute root mean square error (RMSE) and the 95th percentile of vertical elevation errors using four different interpolation methods (linear, inverse distance weighted, natural neighbor, and cell averaging) to resample bare earth lidar and lidar-derived digital elevation models (DEMs) onto unstructured meshes at different resolutions. The results are consolidated into a table of optimal interpolation methods that minimize the vertical elevation error of an unstructured mesh for a given mesh node density. The cell area averaging method performed most accurate when DEM grid cells within 0.25 times the ratio of local element size and DEM cell size were averaged. The methodology is applied to simulate inundation extent and maximum water levels in southern Mississippi due to Hurricane Katrina, which illustrates that local changes in topography such as adjusting element size and interpolation method drastically alter simulated storm surge locally and non-locally. The methods and results presented have utility and implications to any modeling application that uses bare earth lidar.     

岩石圈磁场数据网格化方法研究

选取最小曲率、克里格、改进Shepard、反距离加权和径向基函数等5种网格化数学模型,对小江断裂地磁总强度加密区岩石圈磁场数据进行数据网格化,采用均方根预测误差和插值数据残差均方根等评价指标对网格化结果进行评价,结果表明,克里格插值与反距离加权插值法的精度最高。进一步比较克里格插值与反距离加权插值法的网格化图形质量,结果显示克里格插值网格化过程中兼顾了数据的平滑性和各实测点与待估点之间的空间位置关系,避免了系统误差,得出克里格插值更适用于岩石圈磁场数据网格化的结论。     

Multivariate interpolation and information entropy for optimizing raingauge network in the Mekong River Basin

ABSTRACT

The applicability of multivariate interpolation and information entropy to optimize the raingauge network in the Mekong River Basin (MRB) is investigated. Three different spatial interpolation methods are tested: inverse distance squared (IDS), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). The validated results confirm that the GIDS method outperformed IDS and OK. The application of information entropy together with GIDS on a network of 57 gauges provided the same information content (7.34 nat) as could be obtained using all 6788 gauges in the MRB. Combining this result with meteorological and hydrological indicators revealed that the number of gauges for the optimum raingauge network could be reduced to 40. The results imply good applicability of the proposed method, which may be used to help prioritize efforts and funds to maintain the raingauge network in a given river basin.     

Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio Mountains (southern Italy)

The spatial variability of precipitation has often been a topic of research, since accurate modelling of precipitation is a crucial condition for obtaining reliable results in hydrology and geomorphology. In mountainous areas, the sparsity of the measurement networks makes an accurate and reliable spatialization of rainfall amounts at the local scale difficult. The purpose of this paper is to show how the use of a digital elevation model can improve interpolation processes at the subregional scale for mapping the mean annual and monthly precipitation from rainfall observations (40 years) recorded in a region of 1400 km² in southern Italy. Besides linear regression of precipitation against elevation, two methods of interpolation are applied: inverse squared distance and ordinary cokriging. Cross‐validation indicates that the inverse distance interpolation, which ignores the information on elevation, yields the largest prediction errors. Smaller prediction errors are produced by linear regression and ordinary cokriging. However, the results seem to favour the multivariate geostatistical method including auxiliary information (related to elevation). We conclude that ordinary cokriging is a very flexible and robust interpolation method because it can take into account several properties of the landscape; it should therefore be applicable in other mountainous regions, especially where precipitation is an important geomorphological factor. Copyright © 2005 John Wiley & Sons, Ltd.     

基于虚拟日变台进行地磁矢量数据日变通化方法

流动地震地磁矢量观测是一种获取地震前兆异常的方法,试验研究和观测实例表明,地震孕育引起的地磁场异常变化量级较小,因此在观测、数据处理过程中都要尽可能消除误差提高精度.本文主要从地磁矢量数据日变通化精度方面开展讨论,首先使用反距离加权插值法由泰安、武汉、崇明、杭州4个地磁台观测资料计算出蒙城地磁台相应的虚拟地磁数据,其磁偏角、水平分量、垂直分量三个要素与真实观测值的相关系数分别高达0.9987、0.9946和0.9806,验证了反距离加权插值方法对地磁观测数据空间插值的有效性.其次,选择东部和西部测区分别使用反距离加权插值方法建立各地磁矢量测点位置的虚拟日变台并用其进行通化,实例计算结果表明,该方法可有效提高日变通化精度,对于地磁台站稀疏地区更具实际应用价值.     

数字岩心逆建模理论下的储层参数定量预测方法

数字岩心微观孔隙结构十分复杂,有限元模拟物性参数与弹性参数之间关系是非线性的,直接反演其物性参数准确度低、稳定性差.本文发展了一种数字岩石物理逆建模方法,实现了基于数字岩心的储层参数有效预测.从数字岩心基函数的构建出发,基于有限元方法,计算了一系列具有等间距物性参数值(孔隙度、泥质含量和含水饱和度)的数字岩心弹性参数(体积模量、剪切模量和密度),通过插值算法建立了数字岩心弹性参数三维数据集,从而实现了弹性模量的有限元数值解的快速构建;然后搜索弹性参数的单值等值面,通过等值面的空间交会得到交点,完成储层参数预测.测试结果表明:基于数字岩心逆建模理论的储层参数预测结果与实际模型一致,具有可行性,并且可以通过增加插值点数目提高预测的准确性;孔隙度和泥质含量预测结果稳定性很好,而含水饱和度对噪声的加入较为敏感.     

Performance of geostatistical interpolation methods for modeling sampled data with non-stationary mean

The measured ozone pollution peak in the atmosphere of Mexico City region was considered in order to study the effect of a non-stationary mean of the sampled data in geostatistics interpolation methods. With this objective the local mean value of the sampled data was estimated through a linear regression analysis of their values on the monitoring station’s coordinates. The residuals obtained by removing the data trend are considered as a set of stationary random variables. Several interpolation methods used in geostatistics, such as inverse distance weighted, kriging, and artificial neural networks techniques were considered. In an effort to optimize and evaluate its performance, we fit interpolated values to sampled data, obtaining optimal values for the parameters defining the used model, that means, the values of the parameters that give the lowest mean RMSE between the interpolated value and measured data at 20 stations at 1500 hours for a set of 21 days of December 2001, which was chosen as the training set. The training set is conformed by all the days in December 2001 excepting the days (3,6,9,12,...,27,30) which were considered as the testing set. Once the optimal model is obtained, it is used to interpolate the values at the stations at 1500 hours for the testing days. The RMSE between interpolated and measured values at monitoring stations was also evaluated for these testing values and is shown as a percentage in Table 2. These values and the defined generalization parameter G, can be used to evaluate the performance and the ability of the models to predict and reproduce the peak of ozone concentrations. Scatter plots for testing data are presented for each interpolation method. An interpretation of the ozone pollution levels obtained at 1500 hours at December 21 was given using the wind field that prevailed in the region 1 h before the same day.     

Seismic health monitoring of an instrumented multistory building using the interpolation method

In this paper, a new approach to structural damage localization is presented using as damage feature the interpolation error related to the use of a spline function in modeling the operational deformed shapes of the structure. Statistically significant variations of the interpolation error between the undamaged and the inspection phase indicate the onset of damage. A threshold value of the damage feature is defined in terms of the tolerable probability of false alarm to select variations of the interpolation error because of damage from those due to random sources. The method is successfully applied to a calibrated model of the factor building a real densely instrumented building at the University of California, Los Angeles. Results show that the method is effective for damage localization for both single and multiple locations of damage also in case of responses corrupted by noise. Copyright © 2014 John Wiley & Sons, Ltd.     

利用地质规则块体建模方法的频率域有限元弹性波速度反演

在频率域弹性波有限元正演方程的基础上,依据匹配函数（也就是观测数据和正演数据残差的二次范数）最小的准则,用矩阵压缩存储与LU分解技术来存储和求解频率域正演方程中的大型稀疏复系数矩阵、用可调阻尼因子的Levenberg Marquard方法求解反演方程组,直接求取地下介质的弹性波速度,导出了频率域弹性波有限元最小二乘反演算法. 为了利用地下地质体的分布规律,减少反演所求的未知数个数,本文又提出了规则地质块体建模方法引入到反演中来. 经数值模型验证,在噪声干扰很大（噪声达到50髎）或初始模型与真实模型相差很大的情况下,反演也能取得很满意的效果,证明本方法具有很好的抗噪性与“强壮性”.     

地质统计学在气象要素场插值的实例研究

对两种气象要素场数据分别用距离平方反比法、三次B样条和克里格（Kriging）法插值计算.比较了三种方法结果的差异和当计算场满足不同类型克里格数学假设前提下,普通克里格法（OK）与泛克里格法（UK）插值结果的异同.结果表明：克里格法的误差普遍偏小,且在插值区域峰值处克里格法的最大绝对误差和残差方差均可能较样条的小,说明只要充分了解研究区域特点,恰当选用参数,克里格法有可能得到优于样条的结果,而距离平方反比法和克里格法用全场数据插值不如使用局部数据插值的精度高,则表明内插计算具有局域性.同时还发现,虽然插值场是否满足克里格法假设对插值结果存在影响,但这种影响有时并不重要,它依赖于插值场的性质.     

Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators

Detailed hydrologic models require high‐resolution spatial and temporal data. This study aims at improving the spatial interpolation of daily precipitation for hydrologic models. Different parameterizations of (1) inverse distance weighted (IDW) interpolation and (2) A local weighted regression (LWR) method in which elevation is the explanatory variable and distance, elevation difference and aspect difference are weighting factors, were tested at a hilly setting in the eastern Mediterranean, using 16 years of daily data. The preferred IDW interpolation was better than the preferred LWR scheme in 27 out of 31 validation gauges (VGs) according to a criteria aimed at minimizing the absolute bias and the mean absolute error (MAE) of estimations. The choice of the IDW exponent was found to be more important than the choice of whether or not to use elevation as explanatory data in most cases. The rank of preferred interpolators in a specific VG was found to be a stable local characteristic if a sufficient number of rainy days are averaged. A spatial pattern of the preferred IDW exponents was revealed. Large exponents (3) were more effective closer to the coast line whereas small exponents (1) were more effective closer to the mountain crest. This spatial variability is consistent with previous studies that showed smaller correlation distances of daily precipitation closer to the Mediterranean coast than at the hills, attributed mainly to relatively warm sea‐surface temperature resulting in more cellular convection coastward. These results suggest that spatially variable, physically based parameterization of the distance weighting function can improve the spatial interpolation of daily precipitation. Copyright © 2009 John Wiley & Sons, Ltd.     

基于一种改进凸集投影方法的地震数据同时插值和去噪

基于稀疏反演的地震插值方法是一种重要的插值方法,然而大多数这类方法只针对无噪声数据或者高信噪比数据插值.实际上,地震数据含有各种噪声,使得插值问题变得更加困难.凸集投影方法是一种高效的插值算法,但是对于含噪声数据的插值效果不理想,针对含噪声数据提出的加权凸集投影方法能够实现同时插值和去噪,但是除了最小阈值需要认真选取外,增加一个权重因子来实现去噪功能.本文由迭代阈值算法推导出加权凸集投影方法,证明其是解无约束优化问题的一种方法,加权因子可以看作拟合误差项的系数.本文还提出了一种改进的凸集投影方法,与原始凸集投影方法相比该方法不需要增加任何计算量,只要通过阈值的选择来进行插值和去噪.数值模拟证明了该算法的计算效率,并且对含噪声数据能够实现较好的插值效果;先插值后去噪的结果证明了同时去噪和插值算法的可靠性和稳定性.     

Bilinear surface smoothing for spatial interpolation with optional incorporation of an explanatory variable. Part 2: Application to synthesized and rainfall data

ABSTRACT

The non-parametric mathematical framework of bilinear surface smoothing (BSS) methodology provides flexible means for spatial (two dimensional) interpolation of variables. As presented in a companion paper, interpolation is accomplished by means of fitting consecutive bilinear surface into a regression model with known break points and adjustable smoothing terms defined by means of angles formed by those bilinear surface. Additionally, the second version of the methodology (BSSE) incorporates, in an objective manner, the influence of an explanatory variable available at a considerably denser dataset. In the present study, both versions are explored and illustrated using both synthesized and real world (hydrological) data, and practical aspects of their application are discussed. Also, comparison and validation against the results of commonly used spatial interpolation methods (inverse distance weighted, spline, ordinary kriging and ordinary cokriging) are performed in the context of the real world application. In every case, the method’s efficiency to perform interpolation between data points that are interrelated in a complicated manner was confirmed. Especially during the validation procedure presented in the real world case study, BSSE yielded very good results, outperforming those of the other interpolation methods. Given the simplicity of the approach, the proposed mathematical framework’s overall performance is quite satisfactory, indicating its applicability for diverse tasks of scientific and engineering hydrology and beyond.

Editor Z. W. Kundzewicz; Associate editor A. Carsteanu     

Field survey and modelling of irrigation water quality indices in a Mediterranean island catchment: a comparison between spatial interpolation methods

ABSTRACT

A biannual survey of physico-chemical quality indices of 104 irrigation-water wells located in a cultivated plain of a Mediterranean island catchment was conducted using a multi-parameter probe. The campaign was planned so as to differentiate between the dry and wet seasons. The acquired data constituted the test bed for evaluating the results and the features of four spatial interpolation methods, i.e. ordinary kriging, universal kriging, inverse distance weighted and nearest neighbours, against those of the recently introduced bilinear surface smoothing (BSS). In several cases, BSS outperformed the other interpolation methods, especially during the two-fold cross-validation procedure. The study emphasizes the fact that both in situ measurements and good mathematical techniques for studying the spatial distribution of water quality indices are pivotal to agricultural practice management. In the specific case studied, the spatio-temporal variability of water quality parameters and the need for monitoring were evident, as low irrigation water quality was encountered throughout the study area.     

Analysis of radial basis function interpolation approach

The radial basis function （RBF） interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman＇s RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.     

Combining categorical and continuous spatial information within the Bayesian maximum entropy paradigm

Due to the fast pace increasing availability and diversity of information sources in environmental sciences, there is a real need of sound statistical mapping techniques for using them jointly inside a unique theoretical framework. As these information sources may vary both with respect to their nature (continuous vs. categorical or qualitative), their spatial density as well as their intrinsic quality (soft vs. hard data), the design of such techniques is a challenging issue. In this paper, an efficient method for combining spatially non-exhaustive categorical and continuous data in a mapping context is proposed, based on the Bayesian maximum entropy paradigm. This approach relies first on the definition of a mixed random field, that can account for a stochastic link between categorical and continuous random fields through the use of a cross-covariance function. When incorporating general knowledge about the first- and second-order moments of these fields, it is shown that, under mild hypotheses, their joint distribution can be expressed as a mixture of conditional Gaussian prior distributions, with parameters estimation that can be obtained from entropy maximization. A posterior distribution that incorporates the various (soft or hard) continuous and categorical data at hand can then be obtained by a straightforward conditionalization step. The use and potential of the method is illustrated by the way of a simulated case study. A comparison with few common geostatistical methods in some limit cases also emphasizes their similarities and differences, both from the theoretical and practical viewpoints. As expected, adding categorical information may significantly improve the spatial prediction of a continuous variable, making this approach powerful and very promising.